HHAeXchange - Data Engineer
Requirements
• Design, build, and own scalable data platforms that power analytics, machine learning, regulatory reporting, and mission-critical operational systems. • Develop robust ELT workflows, data models, and distributed processing jobs using modern cloud data technologies. • Implement modern data engineering solutions using technologies such as Python, SQL, dbt, Airflow, Snowflake, Tableau, Power BI, AWS data services, containerization (Docker/Kubernetes), and related cloud-native tools. • Leverage best practices to ensure data quality, integrity, lineage, governance, performance, and reliability across all pipelines, data platforms, and analytics applications. • Establish and maintain SLAs, monitoring, and alerting for mission-critical data services with observability design and implementation. • Identify and resolve performance bottlenecks to ensure scalable and cost-efficient data processing. • Other duties as assigned by supervisor or HHAeXchange leader. • Travel up to 10%, including overnight travel • Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field. • 2+ years of combined software and data engineering experience with a strong focus on distributed data processing and data-intensive applications. • 1+ years of experience implementing, and maintaining reporting and analytics tools (e.g., Tableau, Power BI, DOMO), with knowledge of data visualization best practices. • Experience in Python, SQL, query and database optimization, data pipelines, data modeling, and privacy/security best practices. • Experience with cloud platforms (AWS, Azure, or GCP) and modern data warehousing technologies (Snowflake, BigQuery, Redshift, etc.). • Excellent communication and collaboration skills. • Desired qualification: 1+ year experience in healthcare or homecare technology (EHR, HL7/FHIR) or regulated industries and product development. • Willingness to explore and adopt AI tools responsibly to enhance productivity and innovation in your role
Responsibilities
• Design, build, and own scalable data platforms that power analytics, machine learning, regulatory reporting, and mission-critical operational systems. • Develop robust ELT workflows, data models, and distributed processing jobs using modern cloud data technologies. • Implement modern data engineering solutions using technologies such as Python, SQL, dbt, Airflow, Snowflake, Tableau, Power BI, AWS data services, containerization (Docker/Kubernetes), and related cloud-native tools. • Leverage best practices to ensure data quality, integrity, lineage, governance, performance, and reliability across all pipelines, data platforms, and analytics applications. • Establish and maintain SLAs, monitoring, and alerting for mission-critical data services with observability design and implementation. • Identify and resolve performance bottlenecks to ensure scalable and cost-efficient data processing. • Other duties as assigned by supervisor or HHAeXchange leader.
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